{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "讲师： 沈福利\n",
    "\n",
    "本章节目标\n",
    "\n",
    "* 使用plt.subplot: 绘制多表格图\n",
    "* 泰坦尼克号 多子图的应用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多子图展示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "并排比较数据的不同视图。这些子图可能是插页、绘图网格或其他更复杂的布局，方便数据的比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-white')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用``plt.subplot``: 绘制多表格图\n",
    "使用plt.subplot()函数，它在网格中创建一个子块。采用三个整型参数：行数、列数和要在图标创建的绘图索引，从左上角到右下角依次绘制图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(1, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f95a90>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,3,1) # 2 行 3列的图标 ，最后一个表示具体的位置\n",
    "plt.subplot(2,3,2)\n",
    "plt.subplot(2,3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1, 7):\n",
    "    plt.subplot(2, 3, i)\n",
    "    plt.text(0.5, 0.5, str((2, 3, i)),fontsize=18, ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个函数 ``plt.subplots_adjust`` 可以用于调整不同子图之间的距离\n",
    "\n",
    "下面我们使用面向对象的函数 ``fig.add_subplot()`` 进行绘制图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4) # 设置横向和纵向 距离\n",
    "\n",
    "for i in range(1, 7):\n",
    "    ax = fig.add_subplot(2, 3, i)\n",
    "    ax.text(0.5, 0.5, str((2, 3, i)),fontsize=18, ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们使用 ``plt.subplots_adjust`` 函数中两个参数``hspace`` 和 ``wspace`` 设置图表之间的宽度和高度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上述图标中含有大量的内部标签，下面我们需要优化，去除没有用的内部标签内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过指定 sharex 和 sharey ，图标中子图自动移除内部的标签.下面我们为创建的图标添加数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axes are in a two-dimensional array, indexed by [row, col]\n",
    "for i in range(2):\n",
    "    for j in range(3):\n",
    "        ax[i, j].text(0.5, 0.5, str((i, j)),\n",
    "                      fontsize=18, ha='center')\n",
    "        \n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 泰坦尼克号 分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# 设置字体大小和图表的大小\n",
    "plt.rc('figure',figsize = (16,10))\n",
    "plt.rc('font',size = 15)\n",
    "\n",
    "# 中文乱码问题\n",
    "from matplotlib.font_manager import _rebuild\n",
    "_rebuild()\n",
    "plt.rcParams['font.sans-serif']=[u'SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = pd.read_csv('data/titanic.csv',index_col = 0)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个 1行 2 列的示例对象\n",
    "fig,ax = plt.subplots(1,2,sharex='col',sharey='row')\n",
    "\n",
    "#ax[0]  男性的年龄分布\n",
    "male_age = titanic[titanic['sex']=='male']['age'].dropna()\n",
    "h1 = ax[0].hist(x = male_age)\n",
    "#ax[1]\n",
    "male_age = titanic[titanic['sex']=='female']['age'].dropna()\n",
    "h2 = ax[1].hist(x = male_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
